PSv2: Privatize ClusterCoordinator.cluster attribute since Cluster is not meant for public use.

PiperOrigin-RevId: 348168836
Change-Id: I9abbd50de59861f02139d7472b397b937e1a4e85

tensorflow/python/distribute/coordinator/cluster_coordinator.py

@@ -994,7 +994,7 @@ class ClusterCoordinator(object):
           "currently.")
     self._strategy = strategy
     self._strategy.extended._used_with_coordinator = True
-    self.cluster = Cluster(strategy)
+    self._cluster = Cluster(strategy)
 
   @property
   def strategy(self):
@@ -1067,7 +1067,7 @@ class ClusterCoordinator(object):
     # Slot variables are usually created during function tracing time; thus
     # `schedule` needs to be called within the `strategy.scope()`.
     with self.strategy.scope():
-      return self.cluster.schedule(fn, args=args, kwargs=kwargs)
+      return self._cluster.schedule(fn, args=args, kwargs=kwargs)
 
   def join(self):
     """Blocks until all the scheduled functions have finished execution.
@@ -1088,7 +1088,7 @@ class ClusterCoordinator(object):
         previously scheduled function since the last time an error was thrown or
         since the beginning of the program.
     """
-    self.cluster.join()
+    self._cluster.join()
 
   def done(self):
     """Returns whether all the scheduled functions have finished execution.
@@ -1106,7 +1106,7 @@ class ClusterCoordinator(object):
         previously scheduled function since the last time an error was thrown or
         since the beginning of the program.
     """
-    return self.cluster.done()
+    return self._cluster.done()
 
   def create_per_worker_dataset(self, dataset_fn):
     """Create dataset on workers by calling `dataset_fn` on worker devices.
@@ -1168,7 +1168,7 @@ class ClusterCoordinator(object):
       iterators (that are on the workers).
     """
     input_workers = input_lib.InputWorkers([
-        (w.device_name, [w.device_name]) for w in self.cluster.workers
+        (w.device_name, [w.device_name]) for w in self._cluster.workers
     ])
 
     return _PerWorkerDistributedDataset(dataset_fn, input_workers, self)
@@ -1191,7 +1191,7 @@ class ClusterCoordinator(object):
       objects.
     """
     results = []
-    for w in self.cluster.workers:
+    for w in self._cluster.workers:
       results.append(w._create_resource(fn, args=args, kwargs=kwargs))  # pylint: disable=protected-access
     return PerWorkerValues(tuple(results))
 

tensorflow/python/distribute/coordinator/cluster_coordinator_mpr_test.py

@@ -162,7 +162,7 @@ class ClusterCoordinatorMprTest(test.TestCase):
         if test_join:
           ps_coordinator.join()
         if test_schedule:
-          while ps_coordinator.cluster._closure_queue._error is None:
+          while ps_coordinator._cluster._closure_queue._error is None:
             time.sleep(1)
           ps_coordinator.schedule(worker_fn)
       except errors.UnavailableError:

tensorflow/python/distribute/coordinator/cluster_coordinator_test.py

@@ -575,7 +575,7 @@ class ClusterCoordinatorTest(TestCaseWithErrorReportingThread):
 
   def testDatasetsShuffledDifferently(self):
     # This test requires at least two workers in the cluster.
-    self.assertGreaterEqual(len(self.coordinator.cluster.workers), 2)
+    self.assertGreaterEqual(len(self.coordinator._cluster.workers), 2)
 
     random_seed.set_random_seed(None)
 
@@ -587,12 +587,12 @@ class ClusterCoordinatorTest(TestCaseWithErrorReportingThread):
 
     # Get elements from the first two iterators.
     iterator_1 = distributed_iterator._values[0]
-    iterator_1._rebuild_on(self.coordinator.cluster.workers[0])
+    iterator_1._rebuild_on(self.coordinator._cluster.workers[0])
     iterator_1 = iterator_1.fetch()
     elements_in_iterator_1 = [e.numpy() for e in iterator_1]
 
     iterator_2 = distributed_iterator._values[1]
-    iterator_2._rebuild_on(self.coordinator.cluster.workers[1])
+    iterator_2._rebuild_on(self.coordinator._cluster.workers[1])
     iterator_2 = iterator_2.fetch()
     elements_in_iterator_2 = [e.numpy() for e in iterator_2]
 

tensorflow/python/distribute/coordinator/fault_tolerance_test.py

@@ -175,7 +175,8 @@ class FaultToleranceTest(test.TestCase):  # pylint: disable=missing-docstring
     model.schedule_training_functions(4)
     # Model does infinite training step, so at this moment, we expect to have 2
     # infinite closures inflight, and 2 closures in the queue.
-    while self.cluster_coord.cluster._closure_queue._inflight_closure_count < 2:
+    while (self.cluster_coord._cluster._closure_queue._inflight_closure_count
+           < 2):
       time.sleep(0.1)
     self.assertFalse(self.cluster_coord.done())
     self._restart(downtime_secs=2, job="worker")
@@ -356,7 +357,8 @@ class FaultToleranceTest(test.TestCase):  # pylint: disable=missing-docstring
 
     # Model does infinite training step, so at this moment, we expect to have 2
     # infinite closures inflight, and 8 closures in the queue.
-    while self.cluster_coord.cluster._closure_queue._inflight_closure_count < 2:
+    while (self.cluster_coord._cluster._closure_queue._inflight_closure_count
+           < 2):
       time.sleep(0.1)
     self.assertFalse(self.cluster_coord.done())
     self._cluster.kill_task("worker", 0)
@@ -380,7 +382,8 @@ class FaultToleranceTest(test.TestCase):  # pylint: disable=missing-docstring
 
     # Model does infinite training step, so at this moment, we expect to have 2
     # infinite closures inflight, and 8 closures in the queue.
-    while self.cluster_coord.cluster._closure_queue._inflight_closure_count < 2:
+    while (self.cluster_coord._cluster._closure_queue._inflight_closure_count
+           < 2):
       time.sleep(0.1)
     self.assertFalse(self.cluster_coord.done())
     self._cluster.kill_task("worker", 0)